Mass processing of social media posts has been brought to scientists' attention during the last decade. The massive growth of online social networks, like Twitter and Facebook, have created a need for determining peoples' opinions and moods through these means. This thesis constitutes a research on measuring users' sentiment upon a particular subject by analysing their posts. Establishing an efficient sentiment measurement technique, can be used into estimating popularity of products or persons. For separating subjective from objective posts, a hybrid classifier based on the syntax analysis of texts, is proposed, performing clearly better than existing classifying tools. Moreover, a new sentiment evaluation technique for measuring the polarity and magnitude of posts' sentiment is described and tested over different social media. Results are compared to various real ratings and show that this approach can have a promising accuracy on sentiment establishment of online posts.